Lead Machine Learning Engineer / Applied Scientist

About the Role

We’re looking for a Lead Machine Learning Engineer / Scientist to join our Algorithms and Research team within the ML & AI organization. In this role, you will help shape the reinforcement learning systems that power high-impact experiences across Upwork, including Search & Recommendations and Uma, our AI assistant. You will design and scale advanced reasoning, planning, and retrieval systems that connect research innovation to production outcomes. This is a hands-on, high-ownership role for someone excited to push the frontier of RL, autonomous agents, and applied machine learning on a fast-evolving platform.

Responsibilities

• Design and advance reinforcement learning systems for reasoning and planning, including approaches inspired by Monte Carlo Tree Search, policy and value networks, and modern agentic decision-making methods.
• Build scalable retrieval and decisioning architectures that combine structured and unstructured data, including vector search, knowledge graphs, and retrieval-augmented generation workflows.
• Lead cross-functional efforts to move ML and RL models from research prototypes into reliable production systems with strong performance, robustness, and observability.
• Partner closely with engineering, research, and Trust & Safety teams to improve explainability, interpretability, and risk mitigation across reinforcement learning and agent-based systems.
• Evaluate emerging techniques in reinforcement learning, planning, and LLM-enabled systems, and translate promising innovations into practical applications for Upwork’s platform.
• Mentor engineers and scientists through technical leadership, thoughtful code reviews, and strong software engineering practices that raise quality across the team.
• Deliver high-impact outcomes aligned with organizational goals, while helping create clarity, structure, and momentum across complex cross-functional initiatives.

What It Takes to Catch Our Eye

• Proven experience designing, training, and deploying reinforcement learning systems in production, with deep familiarity in planning methods such as Monte Carlo Tree Search and policy or value-based approaches.
• Strong expertise in machine learning systems that use vector databases, graph databases, knowledge graphs, or graph neural networks to improve reasoning and decision quality.
• Track record of leading technically complex initiatives across research and engineering partners, with the judgment to balance experimentation, scalability, and production reliability.
• Experience applying AI tools and iterative prompt or workflow strategies to accelerate model development, analysis, debugging, or experimentation while maintaining strong technical rigor.
• Passion for building intelligent agent systems that combine reinforcement learning, large language models, and retrieval techniques to solve meaningful product and platform challenges.